"""
基于元学习的自适应市场策略
策略特点：
1. 使用MAML算法快速适应市场变化
2. 支持小样本学习新市场模式
3. 自动调整策略参数适应不同市场状态
"""

import torch
import torch.nn as nn
import higher
from collections import defaultdict

class TradingPolicy(nn.Module):
    """基础交易策略网络"""
    def __init__(self, input_dim=10):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, 32),
            nn.ReLU(),
            nn.Linear(32, 16), 
            nn.ReLU(),
            nn.Linear(16, 2)  # 输出买入/卖出信号
        )
        
    def forward(self, x):
        return self.net(x)

class MetaLearner:
    def __init__(self, market_modes):
        """
        market_modes: 不同市场状态描述列表
        """
        self.meta_model = TradingPolicy()
        self.meta_optim = torch.optim.Adam(self.meta_model.parameters(), lr=0.001)
        self.market_modes = market_modes
        self.mode_embeddings = nn.Embedding(len(market_modes), 16)
        
    def meta_train(self, tasks, meta_batch_size=5, adapt_steps=3):
        """元训练过程"""
        for _ in range(100):  # 元迭代次数
            meta_loss = 0
            for task_batch in self._sample_tasks(tasks, meta_batch_size):
                
                # 内循环适应
                with higher.innerloop_ctx(
                    self.meta_model, self.meta_optim, copy_initial_weights=False
                ) as (fnet, diffopt):
                    
                    # 任务特定适应
                    for _ in range(adapt_steps):
                        x, y = task_batch.sample()
                        loss = nn.CrossEntropyLoss()(fnet(x), y)
                        diffopt.step(loss)
                    
                    # 元梯度计算
                    x, y = task_batch.sample()
                    meta_loss += nn.CrossEntropyLoss()(fnet(x), y)
            
            # 外循环更新
            self.meta_optim.zero_grad()
            meta_loss.backward()
            self.meta_optim.step()
    
    def adapt_to_market(self, market_mode, recent_data, adapt_steps=5):
        """快速适应特定市场状态"""
        mode_idx = self.market_modes.index(market_mode)
        mode_emb = self.mode_embeddings(torch.tensor([mode_idx]))
        
        with higher.innerloop_ctx(
            self.meta_model, self.meta_optim, copy_initial_weights=False
        ) as (fnet, diffopt):
            
            for _ in range(adapt_steps):
                x, y = self._prepare_data(recent_data, mode_emb)
                loss = nn.CrossEntropyLoss()(fnet(x), y)
                diffopt.step(loss)
                
            return fnet
            
    def _prepare_data(self, raw_data, mode_emb):
        """准备训练数据"""
        # 这里应实现具体的数据处理逻辑
        features = torch.randn(32, 10)  # 示例特征
        labels = torch.randint(0, 2, (32,))  # 示例标签
        return features, labels
        
    def _sample_tasks(self, tasks, batch_size):
        """采样任务批次"""
        # 简化实现
        return [tasks[i % len(tasks)] for i in range(batch_size)]

if __name__ == '__main__':
    # 示例用法
    market_modes = ['trending', 'mean_reverting', 'volatile', 'calm']
    strategy = MetaLearner(market_modes)
    
    # 模拟元训练任务
    tasks = [object() for _ in range(10)]  # 应替换为真实任务
    
    # 元训练
    strategy.meta_train(tasks)
    
    # 快速适应新市场
    adapted_model = strategy.adapt_to_market('volatile', recent_data={})
    
    # 使用适应后的模型
    test_input = torch.randn(1, 10)
    print("交易信号:", ['卖出', '买入'][adapted_model(test_input).argmax().item()])